Bashar I. Ahmad

Reliable wideband multichannel spectrum sensing using randomized sampling schemes

A wideband multichannel spectrum sensing approach that utilizes nonuniform sampling and digital alias-free signal processing (DASP) to reliably sense the spectrum using sampling rates well below the ones used in classical DSP is proposed. The approach deploys a periodogram-type spectral analysis tool to estimate the spectrum of the incoming signal from a finite number of its noisy nonuniformly distributed samples. The statistical characteristics of the adopted estimator are analyzed and its accuracy is assessed. It is demonstrated here that owing to the use of nonuniform sampling, the sensing task can be carried out with the use of arbitrary low sampling rates. Most importantly, general guidelines are provided on the required signal analysis time window for a chosen sampling rate to guarantee sensing reliability within a particular scenario. The extra requirement on such recommendations imposed by the presence of noise is given. The analytical results are illustrated by numerical examples. This paper establishes a new framework for multiband spectrum sensing where substantial saving on the used sampling rates can be achieved.

Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles

Using interactive displays, such as a touchscreen, in vehicles typically requires dedicating a considerable amount of visual as well as cognitive capacity and undertaking a hand pointing gesture to select the intended item on the interface. This can act as a distractor from the primary task of driving and consequently can have serious safety implications. Due to road and driving conditions, the user input can also be highly perturbed resulting in erroneous selections compromising the system usability. In this paper, we propose intent-aware displays that utilize a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can be achieved with high confidence remarkably early in the pointing gesture. This can drastically reduce the time and effort required to successfully complete an in-vehicle selection task. In the proposed probabilistic inference framework, the likelihood of all the nominal destinations is sequentially calculated by modeling the hand pointing gesture movements as a destination-reverting process. This leads to a Kalman filter-type implementation of the prediction routine that requires minimal parameter training and has low computational burden; it is also amenable to parallelization. The substantial gains obtained using an intent-aware display are demonstrated using data collected in an instrumented vehicle driven under various road conditions.

Touchscreen usability and input performance in vehicles under different road conditions: an evaluative study

With the proliferation of the touchscreen technology, interactive displays are becoming an integrated part of the modern vehicle environment. However, due to road and driving conditions, the user input on such displays can be perturbed resulting in erroneous selections. This paper describes an evaluative study of the usability and input performance of in-vehicle touchscreens. The analysis is based on data collected in instrumented cars driven under various road/driving conditions. We assess the frequency of failed selection attempts, distances by which users miss the intended on-screen target and the durations of undertaken free hand pointing gestures to accomplish the selection tasks. It is shown that the road/driving conditions can notably undermine the usability of an interactive display when the user input is perturbed, e.g. due to the experienced vibrations and lateral accelerations in the vehicle. The distance between the location of an erroneous on-screen selection and the intended endpoint on the display, is closely related to the level of present in-vehicle noise. The conducted study can advise graphical user interfaces design for the vehicle environment where the user free hand pointing gestures can be subject to varying levels of perturbations.

Filtering perturbed in-vehicle pointing gesture trajectories: Improving the reliability of intent inference

Making a selection on an in-vehicle touchscreen entails undertaking a pointing gesture that can be subjected to a high level of perturbation due to road and/or driving conditions. This can lead to erroneous user input and requires further attention that would otherwise be available for driving. In this paper, we propose a low-complexity sequential Monte Carlo filtering method that removes the perturbations present in a highly non-linear pointing hand/finger trajectory. This latter is tracked using a 3D vision sensory device. The preprocessing introduced allows the intended destination on the interactive display to be determined, which can substantially reduce the duration of the pointing task and associated attention. The benefits of the proposed approach are illustrated using data from in-vehicle tests.

Interactive Displays in Vehicles: Improving Usability with a Pointing Gesture Tracker and Bayesian Intent Predictors

Interactive displays are becoming an integrated part of the modern vehicle environment. Their use typically entails dedicating a considerable amount of attention and undertaking a pointing gesture to select an interface item/icon displayed on a touchscreen. This can have serious safety implications for the driver. The pointing gesture can also be highly perturbed due to the road and driving conditions, resulting in erroneous selections. In this paper, we propose a probabilistic intent prediction approach that facilitates establishing the targeted icon on the interface early in the pointing gesture. It employs a 3D vision sensory device to continuously track the pointing hand/finger in conjunction with suitable Bayesian prediction algorithms. The introduced technique can significantly reduce the pointing task completion time, the necessary associated visual, cognitive and movement efforts as well as enhance the selection accuracy. The substantial furnished gains and the pointing gesture characteristics are demonstrated using data collected in an instrumented vehicle.

A SARS Method for Reliable Spectrum Sensing in Multiband Communication Systems

This paper introduces a novel method of spectrum sensing in communication systems that utilizes nonuniform sampling in conjunction with a suitable spectral analysis tool. It is referred to here as spectral analysis for randomized sampling (SARS). Owing to the deployment of nonuniform sampling, the proposed technique can accomplish the sensing task by using sampling rates well below the ones demanded by uniform-sampling-based digital signal processing (DSP). The effect of the cyclostationary nature of the incoming digital communication signal on the adequacy of the adopted periodogram-type estimator for the spectrum sensing operation is addressed. The statistical characteristics of the estimator are presented. General reliability conditions on the length of the required signal observation window, i.e., sensing time, for a chosen sampling rate or vice versa are provided amid a sought system performance. The impact of the presence of noise and processing transmissions with various power levels on the derived dependability recommendations is given. The analytical results are illustrated by numerical examples. This paper establishes a new framework for efficient spectrum sensing where considerable savings on the sampling rate and number of processed samples can be attained.

Intelligent Interactive Displays in Vehicles with Intent Prediction: A Bayesian framework

Using an in-vehicle interactive display, such as a touch screen, typically entails undertaking a freehand pointing gesture and dedicating a considerable amount of attention, that can be otherwise available for driving, with potential safety implications. Due to road and driving conditions, the users input can also be subject to high levels of perturbations resulting in erroneous selections. In this article, we give an overview of the novel concept of an intelligent predictive display in vehicles. It can infer, notably early in the pointing task and with high confidence, the item the user intends to select on the display from the tracked freehand pointing gesture and possibly other available sensory data. Accordingly, it simplifies and expedites the target acquisition (pointing and selection), thereby substantially reducing the time and effort required to interact with an in-vehicle display. As well as briefly addressing the various signal processing and human factor challenges posed by predictive displays in the automotive environment, the fundamental problem of intent inference is discussed, and a Bayesian formulation is introduced. Empirical evidence from data collected in instrumented cars is shown to demonstrate the usefulness and effectiveness of this solution.

You Do Not Have to Touch to Select: A Study on Predictive In-car Touchscreen with Mid-air Selection

In this paper, we first give an overview of the predictive display concept, which aims to minimise the demand associated with interacting with in-vehicle displays, such as touchscreens, via free hand pointing gestures. It determines the item the user intends to select, early in the pointing gesture, and accordingly simplifies-expedites the target acquisition. A study to evaluate the impact of using a predictive touchscreen in a car is then presented. The mid-air selection pointing facilitation scheme is applied, such that the user does not have to physically touch the interactive surface. Instead, the predictive display auto-selects the predicted interface icon on behalf of the user, once the required level of inference certainty is achieved. The study results, which are based on data collected from 20 participants under various driving-road conditions, demonstrate that a predictive display can significantly reduce the workload, effort and durations of completing on-screen selection tasks in vehicles.

Wideband spectrum sensing technique based on random sampling on grid: Achieving lower sampling rates

This paper presents a method that deploys nonuniform sampling and appropriates to it a processing algorithm to monitor the activity of a number of non-overlapping spectral bands. The proposed approach facilitates the use of sampling rates well below the ones demanded by uniform-sampling-based DSP. Randomized sampling scheme, namely random sampling on grid, in conjunction with a periodogram-type spectral analysis tool is utilized to accomplish the task. The statistical characteristics of the endorsed analysis tool are examined for a finite set of nonuniformly distributed signal samples contaminated with noise. General guidelines are provided to ensure the reliability of the adopted sensing technique where it is affirmed that the sampling rates can be arbitrarily low. The additional requirements on such recommendations imposed by the presence of noise are given. It is demonstrated that in certain scenarios the proposed technique can considerably reduce the complexity of the spectrum sensing procedure. The presented analytical results are illustrated by numerical examples. This paper establishes a new framework for efficient spectrum sensing methods that exploit randomized sampling schemes. Unlike a number of similar approaches in the literature, it offers solutions that are well suited for practical implementation in hardware.

Spectral Analysis of Stratified Sampling: A Means to Perform Efficient Multiband Spectrum Sensing

This paper introduces a multiband spectrum sensing technique that utilizes nonuniform stratified sampling. Capitalising on the ability of the sampling scheme to suppress aliasing, the proposed method accomplishes the sensing task using sampling rates well below the ones demanded by the approaches based on uniform sampling. This effectively eases the stringent sampling rate requirements on the data acquisition module, especially when the spectrum sensing is conducted over wide bandwidths. The statistical characteristics of the adopted periodogram-type spectral analysis tool are examined and subsequently employed to formulate a dependable detection procedure amid a specified system performance. Recommendations are provided to ensure that the proposed technique satisfies the sought detection probabilities. These guidelines address the trade-off between the required sampling rate and the length of the signal observation window (sensing time) in a given scenario, offering the user the means to evaluate the benefits/advantages of the introduced approach. Numerical examples are presented to demonstrate the usefulness and effectiveness of the method. It is illustrated that stratified sampling can be implemented in practice using finite resources. This is a clear advantage over previously reported randomized sampling schemes such as total random sampling and Poisson sampling where the sampling instants can be arbitrarily close, i.e. demand infinitely fast acquisition device(s).